This description relates to predictive modeling.
Predictive modeling, for example applied to targeted marketing, refers to modeling (a) which of a company's customers would likely buy a new or additional product (that is, would be susceptible to a cross-sell or up-sell effort); or (b) which prospects from a population of potential customers would be likely to accept an offer for a product or service (called acquisition by response or look-alike); or (c) which existing customers are most likely to cancel a current service (called retention or chum reduction); or (d) trigger points for behavior outside the normal range; or (e) to estimate the expected value or magnitude of a predicted outcome. Modeling is typically done by an analyst who is either an employee of a company or of an external consulting or service bureau. The analyst uses his experience and skill to create a custom model using available model building software applied to currently available data. The cost and accuracy of the model depend on the ability of the analyst, the time permitted to develop the model, the quality of the data used, and the performance of the model development software tools.
When the model is to be used to select prospects, who will be the targets of, say, a direct mail advertising campaign, the time and expense of creating the model and the quality of the selection produced by the model are important considerations.